The most dangerous model isn't the one that's wrong. It's the one that's wrong AND confident.

I've seen models output predictions with 99% confidence that were completely incorrect. Users trust confident predictions. Systems downstream act on confident predictions. Confident wrong predictions cause more damage than uncertain wrong predictions.

Confidence calibration — making sure that when your model says "95% confident," it's actually right 95% of the time — is one of the most overlooked aspects of production ML.

How to diagnose: plot predicted confidence against actual accuracy. A well-calibrated model should follow the diagonal — 80% confidence predictions should be correct 80% of the time. Most models are overconfident.

How to fix: temperature scaling (dead simple, often effective), Platt scaling for binary classifiers, isotonic regression for more complex cases. These are post-processing steps that take an afternoon to implement.

For LLMs: ask the model to rate its own confidence, then calibrate those ratings against actual correctness. Or better yet, implement a secondary verification system for high-stakes predictions.

The ROI of confidence calibration: users learn to trust your system appropriately. High-confidence predictions are reliable. Low-confidence predictions get human review.

An accurate model with good calibration is worth significantly more than a slightly more accurate model with poor calibration. Calibrate your models.